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Drone-assisted Wireless Rechargeable Sensor Networks (WRSNs) have been widely adopted in various urban applications due to their sustainability and scalability. In drone-assisted WRSN systems, incentive mechanisms are essential to motivate potential drone users to provide sustainable energy replenishment services. This paper presents a system model for drone-assisted WRSN scenarios, formulating the problem as a Social Optimization Drone User Selection (SODUS) problem. We design an incentive mechanism combining Deep Reinforcement Learning (DRL) with greedy optimization. By means of thorough theoretical analysis and extensive simulations, we can show that our proposed mechanism not only attains individual rationality and truthfulness, but also remarkably surpasses the benchmarks in performance.

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Charging Scheduling via Deep Reinforcement Learning: An Incentive Mechanism for Drone-Assisted WRSNs

  • 会议论文

  • 发表日期：

    2025年

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  • Yong Jin,
  • Jianxiao Yin,
  • Hualong Bian

摘要

Drone-assisted Wireless Rechargeable Sensor Networks (WRSNs) have been widely adopted in various urban applications due to their sustainability and scalability. In drone-assisted WRSN systems, incentive mechanisms are essential to motivate potential drone users to provide sustainable energy replenishment services. This paper presents a system model for drone-assisted WRSN scenarios, formulating the problem as a Social Optimization Drone User Selection (SODUS) problem. We design an incentive mechanism combining Deep Reinforcement Learning (DRL) with greedy optimization. By means of thorough theoretical analysis and extensive simulations, we can show that our proposed mechanism not only attains individual rationality and truthfulness, but also remarkably surpasses the benchmarks in performance.